scholarly journals Reliability Predictors for Solar Irradiance Satellite-Based Forecast

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5566
Author(s):  
Sylvain Cros ◽  
Jordi Badosa ◽  
André Szantaï ◽  
Martial Haeffelin
Keyword(s):  
Solar Energy ◽  
Absolute Error ◽  
Skill Score ◽  
Start Time ◽  
Clear Sky ◽  
Accurate Forecast ◽  
Northern Atlantic ◽  
Forecast Skill Score ◽  
Energy Forecast ◽  
Pv Grid

The worldwide growing development of PV capacity requires an accurate forecast for a safer and cheaper PV grid penetration. Solar energy variability mainly depends on cloud cover evolution. Thus, relationships between weather variables and forecast uncertainties may be quantified to optimize forecast use. An intraday solar energy forecast algorithm using satellite images is fully described and validated over three years in the Paris (France) area. For all tested horizons (up to 6 h), the method shows a positive forecast skill score compared to persistence (up to 15%) and numerical weather predictions (between 20% and 40%). Different variables, such as the clear-sky index (Kc), solar zenith angle (SZA), surrounding cloud pattern observed by satellites and northern Atlantic weather regimes have been tested as predictors for this forecast method. Results highlighted an increasing absolute error with a decreasing SZA and Kc. Root mean square error (RMSE) is significantly affected by the mean and the standard deviation of the observed Kc in a 10 km surrounding area. The highest (respectively, lowest) errors occur at the Atlantic Ridge (respectively, Scandinavian Blocking) regime. The differences of relative RMSE between these two regimes are from 8% to 10% in summer and from 18% to 30% depending on the time horizon. These results can help solar energy users to anticipate—at the forecast start time and up to several days in advance—the uncertainties of the intraday forecast. The results can be used as inputs for other solar energy forecast methods.

scholarly journals Forecast skill score assessment of a relocatable ocean prediction system, using a simplified objective analysis method

Ocean Science ◽  
2017 ◽  
Vol 13 (6) ◽  
pp. 925-945 ◽  
Author(s):  
Reiner Onken
Keyword(s):  
Observational Data ◽  
Skill Score ◽  
Forecast Skill ◽  
Objective Analysis ◽  
Prediction System ◽  
Regional Ocean Modeling System ◽  
Data Set ◽  
Vast Number ◽  
Ocean Prediction ◽  
Forecast Skill Score

Abstract. A relocatable ocean prediction system (ROPS) was employed to an observational data set which was collected in June 2014 in the waters to the west of Sardinia (western Mediterranean) in the framework of the REP14-MED experiment. The observational data, comprising more than 6000 temperature and salinity profiles from a fleet of underwater gliders and shipborne probes, were assimilated in the Regional Ocean Modeling System (ROMS), which is the heart of ROPS, and verified against independent observations from ScanFish tows by means of the forecast skill score as defined by Murphy(1993). A simplified objective analysis (OA) method was utilised for assimilation, taking account of only those profiles which were located within a predetermined time window W. As a result of a sensitivity study, the highest skill score was obtained for a correlation length scale C = 12.5 km, W = 24 h, and r = 1, where r is the ratio between the error of the observations and the background error, both for temperature and salinity. Additional ROPS runs showed that (i) the skill score of assimilation runs was mostly higher than the score of a control run without assimilation, (i) the skill score increased with increasing forecast range, and (iii) the skill score for temperature was higher than the score for salinity in the majority of cases. Further on, it is demonstrated that the vast number of observations can be managed by the applied OA method without data reduction, enabling timely operational forecasts even on a commercially available personal computer or a laptop.

Shortest Term Solar Energy Forecast

2015 ◽  
pp. 35-40
Author(s):  
Jan Remund ◽  
Daniel Klauser ◽  
Stefan C. Müller
Keyword(s):  
Solar Energy ◽  
Energy Forecast

Power Converter for Dual-Power PV-Grid Energy System Utilizing Cascaded Multilevel Inverter

2014 ◽  
Vol 925 ◽  
pp. 505-509 ◽  
Author(s):  
Intan Rahayu Ibrahim ◽  
Ahmad Maliki Omar ◽  
Zakaria Hussain
Keyword(s):  
Solar Energy ◽  
Electrical Energy ◽  
Energy System ◽  
Power Converter ◽  
Maximum Power ◽  
Multilevel Inverter ◽  
Grid System ◽  
Peak Period ◽  
Pv Grid ◽  
Dual Power

The dual-power PV-grid system was introduced to manipulate the lower tariff rate at off-peak period and to reduce the capital installation cost of PV energy system. The power converter in the PV energy system is used to process solar energy captured by PV modules into usable electrical energy. In the dual-power PV-grid system, the power converter component is consists of a boost regulator to boost and regulate PV outputs to fixed voltage of 240V, 50Hz, a maximum power point tracker (MPPT) to derive maximum power from PV panels and a three operation modes of the battery converter to regulate charging current/discharging current under various PV output and load variation. In this project, a reduced switch and increased level of cascaded H-bridge multilevel inverter was introduced to convert the direct current (DC) output of the solar energy to alternating current (AC) signal to supply an AC load or to be integrated to the grid system. By adapting selective harmonic elimination (SHE) switching strategy, the inverter produces 21 levels of stepped sinusoidal output signal with resultant total harmonics distortion (THD) of 3.90%.

scholarly journals A Review of Solar PV-Grid Parity in Akure, South-West Nigeria

2015 ◽  
Vol 5 (5) ◽  
pp. 879
Author(s):  
Adegoke Oladipo Melodi ◽  
Sola Richard Famakin
Keyword(s):  
Solar Energy ◽  
Environmental Sustainability ◽  
Alternative Energy ◽  
Unit Cost ◽  
Solar Pv ◽  
Study Region ◽  
Energy Unit ◽  
South West ◽  
Major Factors ◽  
Pv Grid

<p>The abundance of solar energy in Akure, South-West Nigeria and its feasibility as an alternative energy source has been proven. However, cheap, Government subsidized but unreliable grid electricity and high cost of solar equipment are considered the major hindrances to deployment of solar energy for improved power supply and environmental sustainability. An earlier work pointed out realistic pricing of electricity, reduced cost of solar equipment and reduction in solar cell degradation factor as major factors capable of speeding up parity hence, motivating solar energy consumption. It showed that parity is attainable within 14 years. Documented significant improvements in these factors in recent times are the motivations for this review. This review cost-comparatively re-assesses both sources of energy under the prevailing National electricity policy and market realities using simple mathematical and graphical modeling techniques. This is with a view to determining a new timing for parity of solar energy with grid supply. Results showed that solar PV-grid energy cost parity is now attainable within 6 years in the study region. It was also observed that sustained improvement in grid energy unit cost and reduction in cost of solar equipment and accessories may accelerate solar-grid energy parity to less than three years.</p>

scholarly journals Development and Validation of Machine-Learning Clear-Sky Detection Method Using 1-Min Irradiance Data and Sky Imagers at a Polluted Suburban Site, Xianghe

2021 ◽  
Vol 13 (18) ◽  
pp. 3763
Author(s):  
Mengqi Liu ◽  
Xiangao Xia ◽  
Disong Fu ◽  
Jinqiang Zhang
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Qualitative Evaluation ◽  
Absolute Error ◽  
High Temporal Resolution ◽  
Accuracy Score ◽  
Energy Applications ◽  
Clear Sky ◽  
Polluted Sites ◽  
Suburban Site

Clear-sky detection (CSD) is of critical importance in solar energy applications and surface radiative budget studies. Existing CSD methods are not sufficiently validated due to the lack of high-temporal resolution and long-term CSD ground observations, especially at polluted sites. Using five-year high resolution ground-based solar radiation data and visual inspected Total Sky Imager (TSI) measurements at polluted Xianghe, a suburban site, this study validated 17 existing CSD methods and developed a new CSD model based on a machine-learning algorithm (Random Forest: RF). The propagation of systematic errors from input data to the calculated global horizontal irradiance (GHI) is confirmed with Mean Absolute Error (MAE) increased by 99.7% (from 20.00 to 39.93 W·m−2). Through qualitative evaluation, the novel Bright-Sun method outperforms the other traditional CSD methods at Xianghe site, with high accuracy score 0.73 and 0.92 under clear and cloudy conditions, respectively. The RF CSD model developed by one-year irradiance and TSI data shows more robust performance, with clear/cloudy-sky accuracy score of 0.78/0.88. Overall, the Bright-Sun and RF CSD models perform satisfactorily at heavy polluted sites. Further analysis shows the RF CSD model built with only GHI-related parameters can still achieve a mean accuracy score of 0.81, which indicates RF CSD models have the potential in dealing with sites only providing GHI observations.

scholarly journals A Novel Mean Field Game-Based Strategy for Charging Electric Vehicles in Solar Powered Parking Lots

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8517
Author(s):  
Samuel M. Muhindo ◽  
Roland P. Malhamé ◽  
Geza Joos
Keyword(s):  
Nash Equilibrium ◽  
Solar Energy ◽  
Electric Vehicles ◽  
Large Population ◽  
Mean Field ◽  
Mean Field Games ◽  
Parking Lot ◽  
Equal Sharing ◽  
Solar Powered ◽  
Energy Forecast

We develop a strategy, with concepts from Mean Field Games (MFG), to coordinate the charging of a large population of battery electric vehicles (BEVs) in a parking lot powered by solar energy and managed by an aggregator. A yearly parking fee is charged for each BEV irrespective of the amount of energy extracted. The goal is to share the energy available so as to minimize the standard deviation (STD) of the state of charge (SOC) of batteries when the BEVs are leaving the parking lot, while maintaining some fairness and decentralization criteria. The MFG charging laws correspond to the Nash equilibrium induced by quadratic cost functions based on an inverse Nash equilibrium concept and designed to favor the batteries with the lower SOCs upon arrival. While the MFG charging laws are strictly decentralized, they guarantee that a mean of instantaneous charging powers to the BEVs follows a trajectory based on the solar energy forecast for the day. That day ahead forecast is broadcasted to the BEVs which then gauge the necessary SOC upon leaving their home. We illustrate the advantages of the MFG strategy for the case of a typical sunny day and a typical cloudy day when compared to more straightforward strategies: first come first full/serve and equal sharing. The behavior of the charging strategies is contrasted under conditions of random arrivals and random departures of the BEVs in the parking lot.

scholarly journals Multimodel Ensemble Forecasts of Precipitation Based on an Object-Based Diagnostic Evaluation

2020 ◽  
Vol 148 (6) ◽  
pp. 2591-2606 ◽  
Author(s):  
Luying Ji ◽  
Xiefei Zhi ◽  
Clemens Simmer ◽  
Shoupeng Zhu ◽  
Yan Ji
Keyword(s):  
Absolute Error ◽  
Skill Score ◽  
Diagnostic Evaluation ◽  
Ensemble Prediction ◽  
Multimodel Ensemble ◽  
Ensemble Forecasts ◽  
Object Based ◽  
Forecast Technique ◽  
Point To Point ◽  
The Individual

Abstract We analyzed 24-h accumulated precipitation forecasts over the 4-month period from 1 May to 31 August 2013 over an area located in East Asia covering the region 15.05°–58.95°N, 70.15°–139.95°E generated with the ensemble prediction systems (EPS) from ECMWF, NCEP, UKMO, JMA, and CMA contained in the TIGGE dataset. The forecasts are first evaluated with the Method for Object-Based Diagnostic Evaluation (MODE). Then a multimodel ensemble (MME) forecast technique that is based on weights derived from object-based scores is investigated and compared with the equally weighted MME and the traditional gridpoint-based MME forecast using weights derived from the point-to-point metric, mean absolute error (MAE). The object-based evaluation revealed that attributes of objects derived from the ensemble members of the five individual EPS forecasts and the observations differ consistently. For instance, their predicted centroid location is more southwestward, their shape is more circular, and their orientation is more meridional than in the observations. The sensitivity of the number of objects and their attributes to methodological parameters is also investigated. An MME prediction technique that is based on weights computed from the object-based scores, median of maximum interest, and object-based threat score is explored and the results are compared with the ensemble forecasts of the individual EPS, the equally weighted MME forecast, and the traditional superensemble forecast. When using MODE statistics for the forecast evaluation, the object-based MME prediction outperforms all other predictions. This is mainly because of a better prediction of the objects’ centroid locations. When using the precipitation-based fractions skill score, which is not used in either of the weighted MME forecasts, the object-based MME forecasts are slightly better than the equally weighted MME forecasts but are inferior to the traditional superensemble forecast that is based on weights derived from the point-to-point metric MAE.

Generating families of synthetic forecasts of different skills from an existing forecast product

2021 ◽  
Author(s):  
Charles Rougé
Keyword(s):  
Water Management ◽  
Absolute Error ◽  
Skill Score ◽  
Statistical Properties ◽  
Forecast Skill ◽  
Systematic Exploration ◽  
Probabilistic Forecasts ◽  
Hydropower Production ◽  
Skill Improvement ◽  
Inform Decision Making

&lt;p&gt;An emerging literature evaluates the water management benefits of hydroclimatic forecasts with timescales of a few days to several months ahead. These studies rely on existing forecast products, which they compare to one another and to baseline scenarios, such as perfect forecast or usual climate or streamflow conditions. Results compare the different products and baselines, both in terms of forecast skill and in terms of value for water management. Yet, the means to systematically explore the link between forecast skill and value (e.g., in terms of water supply reliability or hydropower production) are hampered by the lack of techniques to generate synthetic forecasts that 1) are realistic in that they present similar statistical properties to existing products, and 2) foster productive two-ways conversations between the analysts and academics who propose new products and those who use them to inform decision-making, so they can determine where to focus further product development efforts.&lt;/p&gt;&lt;p&gt;This work proposes a methodology for generating forecasts from an existing product and existing hydroclimatic records (rainfall, temperature, streamflow&amp;#8230;). It perfects and extends a recent synthetic forecast generation technique that deterministically generates a forecast for a point in the future with the desired bias and accuracy, using a linear combination of the quantity to predict with a predictor. It perfects it by proposing a methodology to generate a family of forecasts with desired skill and bias, for several of the most common skill measures, including mean absolute error and (root) mean square error. Generated synthetic forecasts are therefore based on existing products and retain their statistical properties while presenting improved skill. The skill improvement can apply to the whole forecast or only to targeted conditions, e.g., drought or flood conditions, or forecasts during and for a certain period of the year. This opens the doors to systematic exploration of the benefits of marginal forecast improvements. The technique is also extended to ensemble (or probabilistic) forecasts, to allow for generating synthetic ensembles with targeted improvements to the continuous ranked probability skill score (CRPSS).&lt;/p&gt;

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